OpenRaiser/Intern-Atlas huggingface dataset:
Medical image denoising using convolutional denoising autoencoders
Venue: arXiv cs.CV 2016
Year: 2016

Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These methods are however limited for requirement of large training sample size and high computational costs. In this paper we show that using small sample size, denoising autoencoders constructed using convolutional layers can be used for efficient denoising of medical images. Heterogeneous images can be combined to boost sample size for increased denoising performance. Simplest of networks can reconstruct images with corruption levels so high that noise and signal are not differentiable to human eye.
Multi Query Optimization in GLADE
Venue: arXiv cs.DB 2016
Year: 2016

SQL-on-Hadoop systems, query optimization, data distribution over multiple nodes and parallelization techniques are few of the areas under extreme research these days. Big names like Amazon, Google, Microsoft and many more are working on implementing systems for faster access of data from multiple nodes reducing data mobility and increasing the parallelization. Queries are retrieved and reviewed by the database systems in an efficient way in the least amount of time by the introduction of various parallelization techniques by running the same query in parallel over different nodes carrying the data. Apart from multi-threading parallelization, there is another way of parallelization that can be performed in order to further reduce retrieval time hence improving the efficiency of the system; parallelization on user queries on top of a DBMS/RDBMS. In this paper, we will study one such technique of how multiple queries can run simultaneously on a system in order to increase the efficiency by reducing the data retrieval from the storage. Maximum sharing of workload has been performed by generating optimal and ubiquitous join plans for a set of queries and then fed them to GLADE (Generalized Linear Aggregate Distribution Engine), a scalable distributed system for large scale data analytics. Our main work is centered on generating GLADE join plans for a Multi-Query satisfying maximum number of queries in order to maximize data sharing and minimize data retrieval for each individual query.
Accelerating scientific codes by performance and accuracy modeling
Venue: arXiv cs.CE 2016
Year: 2016

Scientific software is often driven by multiple parameters that affect both accuracy and performance. Since finding the optimal configuration of these parameters is a highly complex task, it extremely common that the software is used suboptimally. In a typical scenario, accuracy requirements are imposed, and attained through suboptimal performance. In this paper, we present a methodology for the automatic selection of parameters for simulation codes, and a corresponding prototype tool. To be amenable to our methodology, the target code must expose the parameters affecting accuracy and performance, and there must be formulas available for error bounds and computational complexity of the underlying methods. As a case study, we consider the particle-particle particle-mesh method (PPPM) from the LAMMPS suite for molecular dynamics, and use our tool to identify configurations of the input parameters that achieve a given accuracy in the shortest execution time. When compared with the configurations suggested by expert users, the parameters selected by our tool yield reductions in the time-to-solution ranging between 10% and 60%. In other words, for the typical scenario where a fixed number of core-hours are granted and simulations of a fixed number of timesteps are to be run, usage of our tool may allow up to twice as many simulations. While we develop our ideas using LAMMPS as computational framework and use the PPPM method for dispersion as case study, the methodology is general and valid for a range of software tools and methods.
A Practical Study of Longitudinal Reference Based Compressed Sensing for MRI
Venue: arXiv cs.IT 2016
Year: 2016

Compressed sensing (CS) is a new signal acquisition paradigm that enables the reconstruction of signals and images from a low number of samples. A particularly exciting application of CS is Magnetic Resonance Imaging (MRI), where CS significantly speeds up scan time by requiring far fewer measurements than standard MRI techniques. Such a reduction in sampling time leads to less power consumption, less need for patient sedation, and more accurate images. This accuracy increase is especially pronounced in pediatric MRI where patients have trouble being still for long scan periods. Although such gains are already significant, even further improvements can be made by utilizing past MRI scans of the same patient. Many patients require repeated scans over a period of time in order to track illnesses and the prior scans can be used as references for the current image. This allows samples to be taken adaptively, based on both the prior scan and the current measurements. Work by Weizman has shown that so-called reference based adaptive-weighted temporal Compressed Sensing MRI (LACS-MRI) requires far fewer samples than standard Compressed Sensing (CS) to achieve the same reconstruction signal-to-noise ratio (RSNR). The method uses a mixture of reference-based and adaptive-sampling. In this work, we test this methodology by using various adaptive sensing schemes, reconstruction methods, and image types. We create a thorough catalog of reconstruction behavior and success rates that is interesting from a mathematical point of view and is useful for practitioners. We also solve a grayscale compensation toy problem that supports the insensitivity of LACS-MRI to changes in MRI acquisition parameters and thus showcases the reliability of LACS-MRI in possible clinical situations.
Monotone Paths in Geometric Triangulations
Venue: arXiv cs.CG 2016
Year: 2016

(I) We prove that the (maximum) number of monotone paths in a geometric triangulation of $n$ points in the plane is $O(1.7864^n)$. This improves an earlier upper bound of $O(1.8393^n)$; the current best lower bound is $Ω(1.7003^n)$. (II) Given a planar geometric graph $G$ with $n$ vertices, we show that the number of monotone paths in $G$ can be computed in $O(n^2)$ time.
Essentials of an Integrated Crowd Management Support System Based on Collective Artificial Intelligence
Venue: arXiv cs.MA 2016
Year: 2016

The simulation of the dynamical behavior of pedestrians and crowds in spatial structures is a consolidated research and application context that still presents challenges for researchers in different fields and disciplines. Despite currently available commercial systems for this kind of simulation are growingly employed by designers and planners for the evaluation of alternative solutions, this class of systems is generally not integrated with existing monitoring and control infrastructures, usually employed by crowd managers and field operators for security reasons. This paper introduces the essentials and the related computational frame- work of an Integrated Crowd Management Support System based on a Collective Artificial Intelligence approach encompassing (i) interfaces from and to monitored and controlled environments (respectively, sen- sors and actuators), (ii) a set of software tools supporting the analysis of pedestrians and crowd phenomena taking place in the environment to feed a (iii) faster than real-time simulation of the plausible evolution of the current situation in order to support forms of inference provid- ing decision support to crowd managers, potentially directly controlling elements of the environment (e.g. blocking turnstiles, escalators), com- municating orders to operators on the field or trying to influence the pedestrians by means of dynamic signage or audible messages.
Geometry-aware Similarity Learning on SPD Manifolds for Visual Recognition
Venue: arXiv cs.CV 2016
Year: 2016

Symmetric Positive Definite (SPD) matrices have been widely used for data representation in many visual recognition tasks. The success mainly attributes to learning discriminative SPD matrices with encoding the Riemannian geometry of the underlying SPD manifold. In this paper, we propose a geometry-aware SPD similarity learning (SPDSL) framework to learn discriminative SPD features by directly pursuing manifold-manifold transformation matrix of column full-rank. Specifically, by exploiting the Riemannian geometry of the manifold of fixed-rank Positive Semidefinite (PSD) matrices, we present a new solution to reduce optimizing over the space of column full-rank transformation matrices to optimizing on the PSD manifold which has a well-established Riemannian structure. Under this solution, we exploit a new supervised SPD similarity learning technique to learn the transformation by regressing the similarities of selected SPD data pairs to their ground-truth similarities on the target SPD manifold. To optimize the proposed objective function, we further derive an algorithm on the PSD manifold. Evaluations on three visual classification tasks show the advantages of the proposed approach over the existing SPD-based discriminant learning methods.
Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities
Venue: arXiv cs.CL 2016
Year: 2016

We study the cohesion within and the coalitions between political groups in the Eighth European Parliament (2014--2019) by analyzing two entirely different aspects of the behavior of the Members of the European Parliament (MEPs) in the policy-making processes. On one hand, we analyze their co-voting patterns and, on the other, their retweeting behavior. We make use of two diverse datasets in the analysis. The first one is the roll-call vote dataset, where cohesion is regarded as the tendency to co-vote within a group, and a coalition is formed when the members of several groups exhibit a high degree of co-voting agreement on a subject. The second dataset comes from Twitter; it captures the retweeting (i.e., endorsing) behavior of the MEPs and implies cohesion (retweets within the same group) and coalitions (retweets between groups) from a completely different perspective. We employ two different methodologies to analyze the cohesion and coalitions. The first one is based on Krippendorff's Alpha reliability, used to measure the agreement between raters in data-analysis scenarios, and the second one is based on Exponential Random Graph Models, often used in social-network analysis. We give general insights into the cohesion of political groups in the European Parliament, explore whether coalitions are formed in the same way for different policy areas, and examine to what degree the retweeting behavior of MEPs corresponds to their co-voting patterns. A novel and interesting aspect of our work is the relationship between the co-voting and retweeting patterns.
Simulation of an Optional Strategy in the Prisoner's Dilemma in Spatial and Non-spatial Environments
Venue: arXiv cs.GT 2016
Year: 2016

This paper presents research comparing the effects of different environments on the outcome of an extended Prisoner's Dilemma, in which agents have the option to abstain from playing the game. We consider three different pure strategies: cooperation, defection and abstinence. We adopt an evolutionary game theoretic approach and consider two different environments: the first which imposes no spatial constraints and the second in which agents are placed on a lattice grid. We analyse the performance of the three strategies as we vary the loner's payoff in both structured and unstructured environments. Furthermore we also present the results of simulations which identify scenarios in which cooperative clusters of agents emerge and persist in both environments.
Large Angle based Skeleton Extraction for 3D Animation
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we present a solution for arbitrary 3D character deformation by investigating rotation angle of decomposition and preserving the mesh topology structure. In computer graphics, skeleton extraction and skeleton-driven animation is an active areas and gains increasing interests from researchers. The accuracy is critical for realistic animation and related applications. There have been extensive studies on skeleton based 3D deformation. However for the scenarios of large angle rotation of different body parts, it has been relatively less addressed by the state-of-the-art, which often yield unsatisfactory results. Besides 3D animation problems, we also notice for many 3D skeleton detection or tracking applications from a video or depth streams, large angle rotation is also a critical factor in the regression accuracy and robustness. We introduced a distortion metric function to quantify the surface curviness before and after deformation, which is a major clue for large angle rotation detection. The intensive experimental results show that our method is suitable for 3D modeling, animation, skeleton based tracking applications.
Practical optimal experiment design with probabilistic programs
Venue: arXiv cs.AI 2016
Year: 2016

Scientists often run experiments to distinguish competing theories. This requires patience, rigor, and ingenuity - there is often a large space of possible experiments one could run. But we need not comb this space by hand - if we represent our theories as formal models and explicitly declare the space of experiments, we can automate the search for good experiments, looking for those with high expected information gain. Here, we present a general and principled approach to experiment design based on probabilistic programming languages (PPLs). PPLs offer a clean separation between declaring problems and solving them, which means that the scientist can automate experiment design by simply declaring her model and experiment spaces in the PPL without having to worry about the details of calculating information gain. We demonstrate our system in two case studies drawn from cognitive psychology, where we use it to design optimal experiments in the domains of sequence prediction and categorization. We find strong empirical validation that our automatically designed experiments were indeed optimal. We conclude by discussing a number of interesting questions for future research.
A unified approach to autocorrelation of Frank, Chu, and Milewski sequences
Venue: arXiv cs.IT 2016
Year: 2016

We construct a family of perfect polyphase sequences that has the Frank sequences, Chu sequences, and Milewski sequences as special cases. This is not the most general construction of this type, but it has a particularly simple form. We also include some remarks about the acyclic autocorrelations of our sequences.
On the Performance of Cell-Free Massive MIMO with Short-Term Power Constraints
Venue: arXiv cs.IT 2016
Year: 2016

In this paper we consider a time-division duplex cell-free massive multiple-input multiple-output (MIMO) system where many distributed access points (APs) simultaneously serve many users. A normalized conjugate beamforming scheme, which satisfies short-term average power constraints at the APs, is proposed and analyzed taking into account the effect of imperfect channel information. We derive an approximate closed-form expression for the per-user achievable downlink rate of this scheme. We also provide, analytically and numerically, a performance comparison between the normalized conjugate beamforming and the conventional conjugate beamforming scheme in [1] (which satisfies long-term average power constraints). Normalized conjugate beamforming scheme reduces the beamforming uncertainty gain, which comes from the users' lack of the channel state information knowledge, and hence, it improves the achievable downlink rate compared to the conventional conjugate beamforming scheme.
SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification
Venue: arXiv cs.CL 2016
Year: 2016

Sentiment in social media is increasingly considered as an important resource for customer segmentation, market understanding, and tackling other socio-economic issues. However, sentiment in social media is difficult to measure since user-generated content is usually short and informal. Although many traditional sentiment analysis methods have been proposed, identifying slang sentiment words remains untackled. One of the reasons is that slang sentiment words are not available in existing dictionaries or sentiment lexicons. To this end, we propose to build the first sentiment dictionary of slang words to aid sentiment analysis of social media content. It is laborious and time-consuming to collect and label the sentiment polarity of a comprehensive list of slang words. We present an approach to leverage web resources to construct an extensive Slang Sentiment word Dictionary (SlangSD) that is easy to maintain and extend. SlangSD is publicly available for research purposes. We empirically show the advantages of using SlangSD, the newly-built slang sentiment word dictionary for sentiment classification, and provide examples demonstrating its ease of use with an existing sentiment system.
Are Today's SDN Controllers Ready for Primetime?
Venue: arXiv cs.NI 2016
Year: 2016

SDN efficiency is driven by the ability of controllers to process small packets based on a global view of the network. The goal of such controllers is thus to treat new flows coming from hundreds of switches in a timely fashion. In this paper, we show this ideal remains impossible through the most extensive evaluation of SDN controllers. We evaluated five state-of-the-art SDN controllers and discovered that the most efficient one spends a fifth of his time in packet serialization. More dramatically, we show that this limitation is inherent to the object oriented design principle of these controllers. They all treat each single packet as an individual object, a limitation that induces an unaffordable per-packet overhead. To eliminate the responsibility of the hardware from our results, we ported these controllers on a network-efficient architecture, Tilera, and showed even worse performance. We thus argue for an in-depth rethinking of the design of the SDN controller into a lower level software that leverages both operating system optimizations and modern hardware features.
A Systematic Approach for Cross-source Point Cloud Registration by Preserving Macro and Micro Structures
Venue: arXiv cs.CV 2016
Year: 2016

We propose a systematic approach for registering cross-source point clouds. The compelling need for cross-source point cloud registration is motivated by the rapid development of a variety of 3D sensing techniques, but many existing registration methods face critical challenges as a result of the large variations in cross-source point clouds. This paper therefore illustrates a novel registration method which successfully aligns two cross-source point clouds in the presence of significant missing data, large variations in point density, scale difference and so on. The robustness of the method is attributed to the extraction of macro and micro structures. Our work has three main contributions: (1) a systematic pipeline to deal with cross-source point cloud registration; (2) a graph construction method to maintain macro and micro structures; (3) a new graph matching method is proposed which considers the global geometric constraint to robustly register these variable graphs. Compared to most of the related methods, the experiments show that the proposed method successfully registers in cross-source datasets, while other methods have difficulty achieving satisfactory results. The proposed method also shows great ability in same-source datasets.
Impact of Artificial Noise on Cellular Networks: A Stochastic Geometry Approach
Venue: arXiv cs.IT 2016
Year: 2016

This paper studies the impact of artificial noise (AN) on the secrecy performance of a target cell in multi-cell cellular networks. Although AN turns out to be an efficient approach for securing a pointto-point/single cell confidential transmission, it would increase the inter-cell interference in a multi-cell cellular network, which may degrade the network reliability and secrecy performance. For analyzing the average secrecy performance of the target cell which is of significant interest, we employ a hybrid cellular deployment model, where the target cell is a circle of fixed size and the base stations (BSs) outside the target cell are modeled as a homogeneous Poisson point process (PPP). We investigate the impact of AN on the reliability and security of users in the target cell in the presence of pilot contamination using a stochastic geometry approach. The analytical results of the average connection outage and the secrecy outage of its cellular user (CU) in the target cell are given, which facilitates the evaluation of the average secrecy throughput of a randomly chosen CU in the target cell. It shows that with an optimized power allocation between the desired signals and AN, the AN scheme is an efficient solution for securing the communications in a multi-cell cellular network.
A Short Counterexample Property for Safety and Liveness Verification of Fault-tolerant Distributed Algorithms
Venue: arXiv cs.LO 2016
Year: 2016

Distributed algorithms have many mission-critical applications ranging from embedded systems and replicated databases to cloud computing. Due to asynchronous communication, process faults, or network failures, these algorithms are difficult to design and verify. Many algorithms achieve fault tolerance by using threshold guards that, for instance, ensure that a process waits until it has received an acknowledgment from a majority of its peers. Consequently, domain-specific languages for fault-tolerant distributed systems offer language support for threshold guards. We introduce an automated method for model checking of safety and liveness of threshold-guarded distributed algorithms in systems where the number of processes and the fraction of faulty processes are parameters. Our method is based on a short counterexample property: if a distributed algorithm violates a temporal specification (in a fragment of LTL), then there is a counterexample whose length is bounded and independent of the parameters. We prove this property by (i) characterizing executions depending on the structure of the temporal formula, and (ii) using commutativity of transitions to accelerate and shorten executions. We extended the ByMC toolset (Byzantine Model Checker) with our technique, and verified liveness and safety of 10 prominent fault-tolerant distributed algorithms, most of which were out of reach for existing techniques.
DNN-based Speech Synthesis for Indian Languages from ASCII text
Venue: arXiv cs.CL 2016
Year: 2016

Text-to-Speech synthesis in Indian languages has a seen lot of progress over the decade partly due to the annual Blizzard challenges. These systems assume the text to be written in Devanagari or Dravidian scripts which are nearly phonemic orthography scripts. However, the most common form of computer interaction among Indians is ASCII written transliterated text. Such text is generally noisy with many variations in spelling for the same word. In this paper we evaluate three approaches to synthesize speech from such noisy ASCII text: a naive Uni-Grapheme approach, a Multi-Grapheme approach, and a supervised Grapheme-to-Phoneme (G2P) approach. These methods first convert the ASCII text to a phonetic script, and then learn a Deep Neural Network to synthesize speech from that. We train and test our models on Blizzard Challenge datasets that were transliterated to ASCII using crowdsourcing. Our experiments on Hindi, Tamil and Telugu demonstrate that our models generate speech of competetive quality from ASCII text compared to the speech synthesized from the native scripts. All the accompanying transliterated datasets are released for public access.